1. Technical Field
The present invention relates to medical image analysis, and more particularly, to a system and method for toboggan-based object detection in cutting planes.
2. Discussion of the Related Art
In the field of medical imaging, various modalities have been developed for generating medical images of anatomical structures for the purposes of screening and evaluating medical conditions. Exemplary modalities include, computed tomography (CT), magnetic resonance (MR), positron emission tomography (PET), ultrasound (US), etc. Each modality provides unique advantages for screening and evaluating certain types of diseases or medical conditions such as colonic polyps, aneurysms, lung nodules, calcification on heart or artery tissue, cancer micro-calcifications, masses in breast tissue, etc.
For example, a CT imaging modality can be used to obtain a set of cross-sectional images or 2D slices of a region or interest (ROI) of a patient for purposes of imaging organs and other anatomies. The CT modality is generally employed for purposes of diagnosing disease because it provides precise images that illustrate the size, shape, and location of various anatomical structures such as organs, soft tissues and bones, and because it enables a more accurate evaluation of lesions and abnormal anatomical structures such as cancer, polyps, etc. One technique for characterizing shapes and segmenting objects generated by CT, MR, PET, US, etc. is known as tobogganing.
Tobogganing is a non-iterative, single-parameter, linear execution time over-segmentation method. It is non-iterative in that it processes each image pixel/voxel only once, thus accounting for the linear execution time. The sole input is an image's discontinuity or local contrast measure, which is used to determine a slide direction at each pixel. However, such a measure does not work in the context of polyp detection from CT image data. To overcome this, a technique for using a toboggan potential to determine a slide direction at each pixel/voxel was developed. Here, the toboggan potential is computed from the original image, in 2D, 3D or higher dimensions, and the potential depends on the application and the objects to be segmented.
In a conventional tobogganing, an entire set of image data is scanned to determine toboggan clusters. However, since locations of objects such as polyps are known in many applications, it is unnecessary to process the entire set of image data. Accordingly, a fast tobogganing algorithm was developed, which starts from a specified location, quickly forms a toboggan cluster locally without involving any pixels/voxels beyond an outer boundary of the toboggan cluster and dynamically and selectively computes its potential when necessary. An example of the conventional tobogganing process and the fast tobogganing process is shown in FIG. 1.
In FIG. 1, a 5×5 toboggan potential map in 2D is shown. Here, numbers represent potential values at each pixel in an ROI. As shown in the map, each pixel slides to its neighbor having a minimal potential value, and then, the pixels slide to two concentration locations, indicated by circles 0 and 1, thereby forming two toboggan clusters. In the conventional tobogganing process, the entire image is scanned to determine the toboggan clusters; however, in the fast tobogganing process, the tobogganing starts from an initial location and a toboggan cluster is formed without involving any pixels/voxels beyond an outer boundary. For example, in FIG. 1, if a pixel with a potential of 8 is selected as an initial location, a cluster concentrated at circle 1 will be formed. The cluster will include only the pixels 8, 6, 18 and 15, thus resulting in a more efficient process.
Automatic object detection algorithms are generally used to help physicians detect spherical and ellipsoidal structures in a large set of image slices by simplifying a complex 3D detection into a simpler 2D detection. This is accomplished by dividing a 3D image into a number of 2D planes, and then, detecting circular structures or bumps in the 2D planes, which are oriented in a number of directions that span an entire image. Information collected from the planes is then combined into a 3D rendering. However, the circular structures are not always separated from other objects by performing a simple or adaptive thresholding. To this end, a watershed segmentation was developed to separate ROIs. However, watershed segmentation algorithms process most of the pixels/voxels in all of the 2D cutting planes in all orientations, thereby causing a computational bottleneck.
Accordingly, there is a need for a segmentation technique that accurately identifies spherical or ellipsoidal structures in a large set of image slices while reducing computational complexity.